System and method for identification of multimedia content elements

ABSTRACT

A system and method for identifying multimedia content elements are presented. The method includes generating a signature for an unknown multimedia content element (MMCE), wherein the unknown MMCE is a portion of a multimedia content item; comparing a signature of the unknown MMCE to a signature cluster of at least one concept to determine whether each of the at least one concepts is proximate to the unknown MMCE; for each proximate concept, determining a probability that the at least one proximate concept identifies the unknown MMCE; and identifying the MMCE based on the determined probabilities of the at least one proximate concept.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/986,241 filed on Apr. 30, 2014, the contents of which are herebyincorporated by reference. This application is a continuation-in-part(CIP) application of U.S. patent application Ser. No. 13/624,397 filedon Sep. 21, 2012, now pending. The Ser. No. 13/624,397 application is aCIP application of:

(a) U.S. patent application Ser. No. 13/344,400 filed on Jan. 5, 2012,now U.S. Pat. No. 8,959,037, which is a continuation of U.S. patentapplication Ser. No. 12/434,221 filed on May 1, 2009, now U.S. Pat. No.8,112,376. The Ser. No. 13/344,400 application is also a CIP of thebelow-referenced U.S. patent application Ser. Nos. 12/195,863 and12/084,150;

(b) U.S. patent application Ser. No. 12/195,863 filed on Aug. 21, 2008,now U.S. Pat. No. 8,326,775, which claims priority under 35 USC 119 fromIsraeli Application No. 185414 filed on Aug. 21, 2007, which is also aCIP of the below-referenced U.S. patent application Ser. No. 12/084,150;and

(c) U.S. patent application Ser. No. 12/084,150 having a filing date ofApr. 7, 2009, now US patent No. Feb. 18, 2014, which is the NationalStage of International Application No. PCT/IL2006/001235 filed on Oct.26, 2005, and Israeli Application No. 173409 filed on Jan. 29, 2006.

All of the applications referenced above are herein incorporated byreference

TECHNICAL FIELD

The present disclosure relates generally to analysis of multimediacontent, and more specifically to methods for recognizing elementsappearing in multimedia content items using probabilistic models.

BACKGROUND

Identification of multimedia content elements shown in multimediacontent is a challenging problem with many practical applications.Currently, available identification systems are mainly used in order torecognize such content. However, prior art systems are very limited intheir ability to identify multimedia content elements that have beenreceived for the first time. In particular, such systems typically focuson determining whether received multimedia content elements match knownmultimedia content elements. As a result, unknown multimedia contentelements (such as those that have been received for the first time) areusually not recognized.

Furthermore, existing solutions are highly sensitive to changes in thereceived multimedia content elements. Occasionally, a small change inthe way a multimedia content element was captured will make itsidentification much more complex and inaccurate. As an example, anidentification system may determine whether a received image of a carincludes a particular make and model of car based on determining whetherthe received image matches a known image of a car of that make andmodel. Existing identification systems often fail to properly identifythe make and model of the car when, e.g., the received image of a carshows the car at a significantly different angle (e.g., from the rearright side) than an angle of the known image of the car (e.g., from thefront left side).

Other solutions for the identification of multimedia content elementsare based on metadata describing the content. However, metadata areoften adequately defined in words that are needed to fully describe themultimedia content (e.g., pictures or video). For example, it may bedesirable to locate a car of a particular model in a large database ofvideo clips or segments. In some cases, the model of the car would bepart of the metadata, but in many cases it would not. Similarly, if apiece of music, as in a sequence of notes, is to be identified, it isnot necessarily the case that in all available content the notes areknown in their metadata form, or for that matter, the search pattern mayjust be a brief audio clip.

It would therefore be advantageous to provide an efficient and accuratesolution for identifying multimedia content elements.

SUMMARY

A summary of several example embodiments of the disclosure follows. Thissummary is provided for the convenience of the reader to provide a basicunderstanding of such embodiments and does not wholly define the breadthof the disclosure. This summary is not an extensive overview of allcontemplated embodiments, and is intended to neither identify key orcritical elements of all aspects nor to delineate the scope of any orall aspects. Its sole purpose is to present some concepts of one or moreembodiments in a simplified form as a prelude to the more detaileddescription that is presented later. For convenience, the term “someembodiments” may be used herein to refer to a single embodiment ormultiple embodiments of the disclosure.

Certain embodiments disclosed herein include a method for identifyingmultimedia content elements. The method comprises generating a signaturefor an unknown multimedia content element (MMCE), wherein the unknownMMCE is a portion of a multimedia content item; comparing a signature ofthe unknown MMCE to a signature cluster of at least one concept todetermine whether each of the at least one concepts is proximate to theunknown MMCE; for each proximate concept, determining a probability thatthe at least one proximate concept identifies the unknown MMCE; andidentifying the MMCE based on the determined probabilities of the atleast one proximate concept.

Certain embodiments disclosed herein include a method for identifyingmultimedia content elements. The method comprises generating a signaturefor an unknown multimedia content element (MMCE), wherein the unknownMMCE is at least a portion of a multimedia content item; determiningwhether the unknown MMCE is identifiable by the signature; upondetermining that the unknown MMCE is identified by the signature,identifying the MMCE based on the signature; and upon determining thatthe MMCE is not identified by the signature: comparing a signature ofthe unknown MMCE to a signature cluster of at least one concept todetermine whether each concept is proximate to the unknown MMCE; foreach proximate concept, determining a probability that the at least oneproximate concept identifies the MMCE; and identifying the MMCE based onthe determined probabilities of the at least one proximate concept.

Certain embodiments disclosed herein include a system for identifyingmultimedia content elements. The system comprises a processing unit; amemory connected to the processing unit, the memory containinginstructions that when executed by the processing unit, configure thesystem to: generate a signature for an unknown multimedia contentelement (MMCE), wherein the unknown MMCE is a portion of a multimediacontent item; compare a signature of the unknown MMCE to a signaturecluster of at least one concept to determine whether each of the atleast one concepts is proximate to the unknown MMCE; for each proximateconcept, determine a probability that the at least one proximate conceptidentifies the unknown MMCE; and identify the MMCE based on thedetermined probabilities of the at least one proximate concept.

Certain embodiments disclosed herein include a system for identifyingmultimedia content elements. The system comprises a processing unit; amemory connected to the processing unit, the memory containinginstructions that when executed by the processing unit, configure thesystem to: generate a signature for an unknown multimedia contentelement (MMCE), wherein the unknown MMCE is at least a portion of amultimedia content item; determine whether the unknown MMCE isidentifiable by the signature; upon determining that the unknown MMCE isidentified by the signature, identify the MMCE based on the signature;and upon determining that the MMCE is not identified by the signature:compare a signature of the unknown MMCE to a signature cluster of atleast one concept to determine whether each concept is proximate to theunknown MMCE; for each proximate concept, determine a probability thatthe at least one proximate concept identifies the MMCE; and identify theMMCE based on the determined probabilities of the at least one proximateconcept.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter disclosed herein is particularly pointed out anddistinctly claimed in the claims at the conclusion of the specification.The foregoing and other objects, features, and advantages of thedisclosed embodiments will be apparent from the following detaileddescription taken in conjunction with the accompanying drawings.

FIG. 1 is a diagram of a network system utilized to describe the variousdisclosed embodiments;

FIG. 2 is a flowchart illustrating a process for identifying multimediacontent elements shown in multimedia content items according to anembodiment;

FIG. 3 is a flowchart illustrating a method for determining whichconcepts have a high probability of accurately identifying a multimediacontent element according to an embodiment;

FIG. 4 is a block diagram illustrating the basic flow of information ina signature generator system; and

FIG. 5 is a diagram illustrating the flow of patches generation,response vector generation, and signature generation in a large-scalespeech-to-text system.

DETAILED DESCRIPTION

It is important to note that the embodiments disclosed herein are onlyexamples of the many advantageous uses of the innovative teachingsherein. In general, statements made in the specification of the presentapplication do not necessarily limit any of the various claimedembodiments. Moreover, some statements may apply to some inventivefeatures but not to others. In general, unless otherwise indicated,singular elements may be in plural and vice versa with no loss ofgenerality. In the drawings, like numerals refer to like parts throughseveral views.

FIG. 1 shows an exemplary and non-limiting diagram of a network system100 utilized to describe the various disclosed embodiments. A network110 is used to communicate between different parts of the network system100. The network 110 may be the Internet, the world-wide-web (WWW), alocal area network (LAN), a wide area network (WAN), a metro areanetwork (MAN), and other networks capable of enabling communicationbetween the elements of the system 100.

Further connected to the network 110 is a user device 120 configured toexecute at least one application 125. The application 125 may be, forexample, a web browser, a script, a mobile or native application(“app”), a web application, or any application programmed to interact orcommunicate with a server 130. The user device 120 may be, but is notlimited to, a personal computer (PC), a personal digital assistant(PDA), a mobile phone, a smart phone, a tablet computer, a laptop, awearable computing device, or another kind of computing device equippedwith browsing, viewing and managing capabilities that is enabled asfurther discussed herein below. It should be noted that only one userdevice 120 and one application 125 are illustrated in FIG. 1 only forthe sake of simplicity and without limitation on the generality of thedisclosed embodiments.

The network system 100 also includes a data warehouse 160 configured tostore multimedia content items, multimedia content elements, concepts,concept structures, and the like. In the embodiment illustrated in FIG.1, the server 130 communicates with the data warehouse 160 through thenetwork 110. In other non-limiting embodiments, the server 130 isdirectly connected to the data warehouse 160.

The various embodiments disclosed herein are realized using the server130, a signature generator system (SGS) 140, and adeep-content-classification (DCC) system 150. The SGS 140 and/or the DCCsystem 150 may be connected to the server 130 directly or through thenetwork 110. The server 130 is configured to receive and serve the atleast one multimedia content item in which multimedia content elementsto be identified are included. The multimedia content item may be, butis not limited to, an image, a graphic, a video stream, a video clip, avideo frame, a photograph, an audio clip, and/or combinations thereofand portions thereof.

In one embodiment, the server 130 is configured to receive a URL of aweb-page viewed by the user device 120 and accessed by the application125. The web-page is processed by the server 130 to extract themultimedia content item contained therein. The request to analyze themultimedia content item can be sent by a script executed in the web-pagesuch as the application 125 (e.g., a web server or a publisher server)when requested to upload one or more multimedia content items to theweb-page. Such a request may include a URL of the web-page or a copy ofthe web-page. The application 125 can also send a picture or a videoclip taken by a user of the user device 120 to the server 130.

In an embodiment, the SGS 140 is configured to generate at least onesignature for each of the multimedia content elements. To this end,according to some disclosed embodiments, the received element ispartitioned into a plurality of elements of content. For each suchelement, at least one signature is generated. As an example, an image(multimedia content element) showing a smiling child with a Ferris wheelin the background is received. The content elements of the image mayinclude the Ferris wheel and the child. Signatures may be generated forthe image respective of the Ferris wheel and of the child using theprocess of signature generation disclosed in greater detail below withrespect to FIGS. 4 and 5.

In an embodiment, the DCC system 150 is queried by the server 130 toidentify the multimedia content elements respective of their signatures.A multimedia content element may be identified respective of itssignature by, e.g., identifying a multimedia content element that isidentical to the other multimedia content element. Upon determinationthat at least one multimedia content element is not identified, the DCCsystem 150 is queried again by the server 130 to find at least oneconcept that matches one or more of the multimedia content elements thatwere not identified. Initially attempting to identify multimedia contentelements based on their signatures advantageously may reduce consumptionof computing resources by only requiring determination of probabilitiesupon determining that at least one multimedia content element isunidentified.

It should be noted that the server 130 typically comprises a processingunit and a memory (not shown). The processing unit is coupled to thememory, which is configured to contain instructions that can be executedby the processing unit. The server also includes a network interface tothe network.

In an embodiment, the processing unit may comprise, or be a componentof, a larger processing unit implemented with one or more processors.The one or more processors may be implemented with any combination ofgeneral-purpose microprocessors, microcontrollers, digital signalprocessors (DSPs), field programmable gate array (FPGAs), programmablelogic devices (PLDs), controllers, state machines, gated logic, discretehardware components, dedicated hardware finite state machines, or anyother suitable entities that can perform calculations or othermanipulations of information.

A concept is a collection of signatures representing a multimediaelement and metadata describing the concept. The collection is asignature reduced cluster generated by inter-matching of the signaturesgenerated for many multimedia content elements, clustering theinter-matched signatures, and providing a reduced cluster set of suchclusters. As a non-limiting example, a ‘Superman concept’ is a signaturereduced cluster of signatures describing elements (such as multimediaelements) related to, e.g., a Superman cartoon: a set of metadataincluding textual representations of the Superman concept.

Techniques for generating concepts and concept structures are alsodescribed in the U.S. Pat. No. 8,266,185 (hereinafter the '185 patent)to Raichelgauz, et al., which is assigned to a common assignee, and isincorporated by reference herein for all that it contains. In anembodiment, the DCC system 150 is configured and operates as the DCCsystem discussed in the '185 patent.

A concept may match an unidentified multimedia content element if asignature cluster of the concept matches a signature of the unidentifiedmultimedia content element above a predefined matching threshold. Asignature cluster and a signature may be matched as two signatures arematched. Signature matching is described further herein below withrespect to FIGS. 4 and 5. The server 130 extracts at least one conceptin proximity to one or more of the unidentified multimedia contentelements. The extraction may be from a data warehouse, for example thedata warehouse 160.

A concept is in proximity to a multimedia content element if a signaturecluster associated with the concept matches one or more signatures ofthe other concept above a predefined proximity threshold. As anon-limiting example, a concept may be proximate to a multimedia contentelement if a clustered signature of the concept matches a signature ofthe multimedia content element above 10%.

The server 130 determines the probability that at least one portion ofan extracted concept matches each of the at least one unidentifiedmultimedia content elements. In an embodiment, the probability may beequal to a signature matching score of the signature cluster of theproximate concept to the unidentified multimedia content element. As anon-limiting example, if a signature matching score of 20% is determinedvia signature matching between the signature cluster and the signature,the probability may be determined to be 20%.

As a non-limiting example of determining probabilities, an image(multimedia content element) showing a pencil and a highlighter isanalyzed. The pencil is identified based on a known concept of pencils.The highlighter remains unidentified. The signature of the highlighteris compared to various concept structures. The comparison yields asignature matching score of 30%. It is determined that the signature ofthe highlighter matches the concept of pencils above the predefinedproximity threshold of 25%. Accordingly, the concept of pencils isdetermined to be proximate to the highlighter multimedia contentelement, and the probability is determined to be 30%.

In an embodiment, the extracted concept that is determined to have thehighest probability may be determined to identify the multimedia contentelement. This may be particularly important when, e.g., multipleconcepts are determined to be proximate to the multimedia contentelement. In an optional embodiment, the highest probability is comparedto an identification threshold. The identification threshold istypically a percentage or decimal value representing a minimalacceptable probability. In that embodiment, if the highest probabilityamong extracted concepts is below the identification threshold, themultimedia content element may be identified as unknown.

It should be noted that each of the server 130, the SGS 140, and the DCCsystem 150 typically includes a processing unit, such as a processor(not shown) or an array of processors, coupled to a memory. In oneembodiment, the processing unit may be realized through architecture ofcomputational cores described in detail below. The memory containsinstructions that can be executed by the processing unit. The server 130also includes an interface (not shown) to the network 110.

In some non-limiting embodiments, the operation of the server 130 isexecuted locally on the user device 120. In an embodiment, the userdevice 120 comprises at least a partial DCC system (not shown in FIG. 1as part of device 120) as well as a partial signature generation system(not shown in FIG. 1 as part of device 120).

FIG. 2 depicts an exemplary and non-limiting flowchart 200 describing amethod for identifying multimedia content elements shown in a multimediacontent item according to an embodiment. In an embodiment, the methodmay be performed by a server (e.g., the server 130) or by a user device(e.g., the user device 120).

In S210, a multimedia content item having at least one multimediacontent element is received. In an embodiment, the multimedia contentitem is received together with a request to identify the multimediacontent elements shown in the multimedia content item.

In S220, at least one signature is generated respective of eachmultimedia content element. In an embodiment, signatures may begenerated by the SGS 140 as described in greater detail herein belowwith respect to FIGS. 4 and 5.

In S230, a DCC system (e.g., the DCC system 150) is queried to identifyeach of the multimedia content elements. According to one embodiment,the identification may be made through a data warehouse (e.g., the datawarehouse 160). According to another embodiment, the identification ismade through one or more data sources accessible over a network (e.g.,the network 110).

In S240, it is checked whether all multimedia content elements wereidentified and, if so, execution continues with S280; otherwise,execution continues with S250. In S250, the DCC system is queried tofind at least one concept that is proximate to each of the unidentifiedmultimedia content elements. Concepts and proximity to multimediacontent elements are described further herein above with respect to FIG.1.

In S260, at least one concept in proximity to the at least oneunidentified multimedia content element is extracted from, for example,the data warehouse 160. A concept is in proximity to a multimediacontent element if a signature cluster associated with the conceptmatches one or more signatures of the other concept above a predefinedproximity threshold. As a non-limiting example, a concept may beproximate to a multimedia content element if a clustered signature ofthe concept has a matching score with a signature of the multimediacontent element that is above 10%.

In S270, the probability that each extracted concept matches each of theone or more unidentified multimedia content elements is determined.Determining probabilities that concepts match unidentified multimediacontent elements is described further herein above with respect to FIG.1.

In S280, it is checked whether additional multimedia content items havebeen received and, if so, execution continues with S210; otherwise,execution terminates.

FIG. 3 is an exemplary and non-limiting flowchart 300 illustrating amethod for determining which concepts have a high probability ofaccurately identifying a multimedia content element according to anembodiment. In S310, a request to determine which concepts have a highprobability of accurately identifying a multimedia content element(MMCE) is received. In an embodiment, the request may further includethe concepts and the multimedia content element.

In S320, a signature is generated for the multimedia content element. InS330, the generated signature is compared to a cluster of signatures ofeach concept to determine whether each concept is in proximity to themultimedia content element. Signature matching is described furtherherein below with respect to FIGS. 4 and 5.

In S340, a probability that a portion of each proximate conceptrepresents the multimedia content element is determined. In anembodiment, the probability is equal to a matching score between theconcept and the unidentified multimedia content element. Determinationof probabilities is described further herein above with respect to FIG.1.

In S350, the concept having the highest probability among proximateconcepts is determined to identify the multimedia content element. In anembodiment, more than one concept having higher probabilities than theother concepts may be identified. As a non-limiting example, a setnumber of concepts (e.g., 3 concepts) having the highest probabilitiesamong concepts may be identified. In that example, among concepts withprobabilities of 0.3, 0.4, 0.55, 0.67, and 0.99, respectively, theconcepts with probabilities of 0.55, 0.67, and 0.99 may be identified.

In another embodiment, if no concept is proximate to the unidentifiedmultimedia content element, that multimedia content element may beidentified as unknown. As a non-limiting example, if the proximitythreshold is 0.6, a multimedia content element with a matching score of0.5 to just one concept structure will be identified as unknown.

As a non-limiting example, a request to determine which concepts have ahigh probability of accurately identifying a multimedia content elementis received. The request contains a multimedia content element featuringa dog and a dog chew toy as well as a concept representing dog chewtoys. A signature is generated for the multimedia content element of thedog and for the dog chew toy. The generated signatures are compared tothe cluster of signatures of the dog chew toy concept. Based on thiscomparison, it is determined that the dog chew toy concept identifiesthe dog chew toy multimedia content element and that the dog multimediacontent element is unidentified. A signature of the dog multimediacontent element is compared to a signature cluster of the dog chew toyconcept, thereby yielding a matching score of 40%, which is above aproximity threshold of 15%. As a result, the probability is determinedto be 40%, and the multimedia content element featuring a dog isidentified as being a dog.

As another non-limiting example, a request to determine which conceptshave a high probability of accurately identifying a multimedia contentelement is received. The request contains a multimedia content elementfeaturing a cookie and a book as well as a concept representing cookies.A signature is generated for the multimedia content element of thecookie and for the book. The generated signatures are compared to thecluster of signatures of the cookie concept. Based on this comparison,it is determined that the cookie concept identifies the cookiemultimedia content element and that the book multimedia content elementis unidentified. A signature of the book multimedia content element iscompared to a signature cluster of the cookie concept, thereby yieldinga matching score of 1%, which is below a proximity threshold of 15%. Asa result, the book is identified as unknown.

FIGS. 4 and 5 illustrate the generation of signatures for the multimediacontent items by the SGS 140 according to one embodiment. An exemplaryhigh-level description of the process for large scale matching isdepicted in FIG. 4. In this example, the matching is conducted based onvideo content.

Video content segments 2 from a Master database (DB) 6 and a Target DB 1are processed in parallel by a large number of independent computationalcores 3 that constitute an architecture for generating the Signatures(hereinafter the “Architecture”). Further details on the generation ofcomputational cores are provided below. The independent cores 3 generatea database of Robust Signatures and Signatures 4 for Targetcontent-segments 5 and a database of Robust Signatures and Signatures 7for Master content-segments 8. An exemplary and non-limiting process ofsignature generation for an audio component is shown in detail in FIG.4. Finally, Target Robust Signatures and/or Signatures are effectivelymatched, by a matching algorithm 9, to Master Robust Signatures and/orSignatures database to find all matches between the two databases.

To demonstrate an example of the signature generation process, it isassumed, merely for the sake of simplicity and without limitation on thegenerality of the disclosed embodiments, that the signatures are basedon a single frame, leading to certain simplification of thecomputational cores generation. The Matching System is extensible forsignatures generation capturing dynamics in-between the frames.

The Signatures' generation process is now described with reference toFIG. 5. The first step in the process of signatures generation from agiven speech-segment is to breakdown the speech-segment to K patches 14of random length P and random position within the speech segment 12. Thebreakdown is performed by the patch generator component 21. The value ofthe number of patches K, random length P, and random position parametersis determined based on optimization, considering the tradeoff betweenaccuracy rate and the number of fast matches required in the flowprocess of the server 130 and SGS 140. Thereafter, all the K patches areinjected in parallel into all computational cores 3 to generate Kresponse vectors 22, which are fed into a signature generator system 23to produce a database of Robust Signatures and Signatures 4.

In order to generate Robust Signatures, i.e., Signatures that are robustto additive noise L (where L is an integer equal to or greater than 1)by the computational cores 3 a frame T is injected into all the cores 3.Then, cores 3 generate two binary response vectors: {right arrow over(S)}, which is a Signature vector, and {right arrow over (RS)} which isa Robust Signature vector.

For generation of signatures robust to additive noise, such asWhite-Gaussian-Noise, scratch, etc., but not robust to distortions, suchas crop, shift and rotation, etc., a core Ci={ni} (1≦i≦L) may consist ofa single leaky integrate-to-threshold unit (LTU) node or more nodes. Thenode ni equations are:

$V_{i} = {\sum\limits_{j}\; {w_{ij}k_{j}}}$ n_(i) = ⊓(Vi − Th_(x))

where, Π is a Heaviside step function; w_(ij) is a coupling node unit(CNU) between node i and image component j (for example, grayscale valueof a certain pixel j); k_(j) is an image component T (for example,grayscale value of a certain pixel j); Thx is a constant Thresholdvalue, where ‘x’ is ‘S’ for Signature and ‘RS’ for Robust Signature; andVi is a Coupling Node Value.

The Threshold values Thx are set differently for Signature generationthan for Robust Signature generation. For example, for a certaindistribution of Vi values (for the set of nodes), the thresholds forSignature (Th_(S)) and Robust Signature (Th_(RS)) are set apart, afteroptimization, according to at least one or more of the followingcriteria:

1: For: V_(i)>Th_(RS)

1−p(V>Th _(S))−1−(1−ε)^(l)>>1

i.e., given that l nodes (cores) constitute a Robust Signature of acertain image I, the probability that not all of these I nodes willbelong to the Signature of same, but noisy image, Ĩ is sufficiently low(according to a system's specified accuracy).

2: p(V_(i)>Th_(RS))≈l/L

i.e., approximately l out of the total L nodes can be found to generatea Robust Signature according to the above definition.

3: Both Robust Signature and Signature are generated for certain framei.

It should be understood that the generation of a signature isunidirectional, and typically yields lossless compression, where thecharacteristics of the compressed data are maintained but theuncompressed data cannot be reconstructed. Therefore, a signature can beused for the purpose of comparison to another signature without the needfor comparison to the original data. The detailed description of theSignature generation can be found in U.S. Pat. Nos. 8,326,775 and8,312,031, assigned to common assignee, which are hereby incorporated byreference for all the useful information they contain.

A computational core generation is a process of definition, selection,and tuning of the parameters of the cores for a certain realization in aspecific system and application. The process is based on several designconsiderations, such as:

(a) The cores should be designed so as to obtain maximal independence,i.e., the projection from a signal space should generate a maximalpair-wise distance between any two cores' projections into ahigh-dimensional space.

(b) The cores should be optimally designed for the type of signals,i.e., the cores should be maximally sensitive to the spatio-temporalstructure of the injected signal, for example, and in particular,sensitive to local correlations in time and space. Thus, in some cases,a core represents a dynamic system, such as in state space, phase space,edge of chaos, etc., which is uniquely used herein to exploit itsmaximal computational power.

(c) The cores should be optimally designed with regard to invariance toa set of signal distortions, of interest in relevant applications.

A detailed description of the computational core generation and theprocess for configuring such cores is discussed in more detail in U.S.patent application Ser. No. 12/084,150 referenced above.

The various embodiments disclosed herein can be implemented as hardware,firmware, software, or any combination thereof. Moreover, the softwareis preferably implemented as an application program tangibly embodied ona program storage unit or computer readable medium consisting of parts,or of certain devices and/or a combination of devices. The applicationprogram may be uploaded to, and executed by, a machine comprising anysuitable architecture. Preferably, the machine is implemented on acomputer platform having hardware such as one or more central processingunits (“CPUs”), a memory, and input/output interfaces. The computerplatform may also include an operating system and microinstruction code.The various processes and functions described herein may be either partof the microinstruction code or part of the application program, or anycombination thereof, which may be executed by a CPU, whether or not sucha computer or processor is explicitly shown. In addition, various otherperipheral units may be connected to the computer platform such as anadditional data storage unit and a printing unit. Furthermore, anon-transitory computer readable medium is any computer readable mediumexcept for a transitory propagating signal.

All examples and conditional language recited herein are intended forpedagogical purposes to aid the reader in understanding the principlesof the disclosed embodiment and the concepts contributed by the inventorto furthering the art, and are to be construed as being withoutlimitation to such specifically recited examples and conditions.Moreover, all statements herein reciting principles, aspects, andembodiments of the disclosed embodiments, as well as specific examplesthereof, are intended to encompass both structural and functionalequivalents thereof. Additionally, it is intended that such equivalentsinclude both currently known equivalents as well as equivalentsdeveloped in the future, i.e., any elements developed that perform thesame function, regardless of structure.

What is claimed is:
 1. A computerized method for identifying multimediacontent elements, comprising: generating a signature for an unknownmultimedia content element (MMCE), wherein the unknown MMCE is a portionof a multimedia content item; comparing a signature of the unknown MMCEto a signature cluster of at least one concept to determine whether eachof the at least one concepts is proximate to the unknown MMCE; for eachproximate concept, determining a probability that the at least oneproximate concept identifies the unknown MMCE; and identifying the MMCEbased on the determined probabilities of the at least one proximateconcept.
 2. The method of claim 1, further comprising: determiningwhether a highest probability of the determined probabilities is abovean identification threshold; and upon determining that the highestprobability is above the identification threshold, identifying the MMCEas known.
 3. The method of claim 1, wherein identifying the MMCE furthercomprises: determining a plurality of proximate concepts having thehighest probabilities among the at least one proximate concept, whereinthe MMCE is identified as potentially being related to each of the atleast one proximate concept.
 4. The method of claim 3, wherein each ofthe at least one proximate concept is determined respective of otherknown MMCEs included in the multimedia content item.
 5. The method ofclaim 1, wherein the comparison yields a matching score, wherein theprobability is equal to the matching score.
 6. The method of claim 1,wherein the identified MMCE is utilized to create a new concept.
 7. Themethod of claim 1, wherein the multimedia content item is any of: animage, a graphic, a video stream, a video clip, a video frame, an audioclip, and a photograph.
 8. A non-transitory computer readable mediumhaving stored thereon instructions for causing at least one processingunit to execute the method according to claim
 1. 9. A method foridentifying multimedia content elements, comprising: generating asignature for an unknown multimedia content element (MMCE), wherein theunknown MMCE is at least a portion of a multimedia content item;determining whether the unknown MMCE is identifiable by the signature;upon determining that the unknown MMCE is identified by the signature,identifying the MMCE based on the signature; and upon determining thatthe MMCE is not identified by the signature: comparing a signature ofthe unknown MMCE to a signature cluster of at least one concept todetermine whether each concept is proximate to the unknown MMCE; foreach proximate concept, determining a probability that the at least oneproximate concept identifies the MMCE; and identifying the MMCE based onthe determined probabilities of the at least one proximate concept. 10.The method of claim 9, wherein identifying the unknown MMCE furthercomprises: determining whether a highest probability among thedetermined probabilities is below a predefined identification threshold;and upon determining that the highest probability is below thepredefined identification threshold, identifying the MMCE as unknown.11. The method of claim 9, wherein identifying the unknown MMCE furthercomprises: determining a plurality of proximate concepts having thehighest probabilities among the at least one proximate concept, whereinthe MMCE is identified as potentially being related to each of theplurality of proximate concepts.
 12. The method of claim 9, wherein eachprobability is determined to be equal to a proportion between the numberof signatures of the proximate concept that match the signature of theMMCE and the total number of signatures of the proximate concept. 13.The method of claim 9, wherein the multimedia content item is any of:image, a graphic, a video stream, a video clip, a video frame, and aphotograph.
 14. A non-transitory computer readable medium having storedthereon instructions for causing one or more processing units to executethe method according to claim
 9. 15. A system for identifying multimediacontent elements, comprising: a processing unit; a memory connected tothe processing unit, the memory containing instructions that whenexecuted by the processing unit, configure the system to: generate asignature for an unknown multimedia content element (MMCE), wherein theunknown MMCE is a portion of a multimedia content item; compare asignature of the unknown MMCE to a signature cluster of at least oneconcept to determine whether each of the at least one concepts isproximate to the unknown MMCE; for each proximate concept, determine aprobability that the at least one proximate concept identifies theunknown MMCE; and identify the MMCE based on the determinedprobabilities of the at least one proximate concept.
 16. The system ofclaim 15, further configured to: determine whether a highest probabilityof the determined probabilities is above an identification threshold;and upon determining that the highest probability is above theidentification threshold, identify the MMCE as known.
 17. The system ofclaim 15, wherein identifying the MMCE further comprises: determining aplurality of proximate concepts having the highest probabilities amongthe at least one proximate concept, wherein the MMCE is identified aspotentially being related to each of the at least one proximate concept.18. The system of claim 17, wherein each of the at least one proximateconcept is determined respective of other known MMCEs included in themultimedia content item.
 19. The system of claim 15, wherein thecomparison yields a matching score, wherein the probability is equal tothe matching score.
 20. The system of claim 15, wherein the identifiedMMCE is utilized to create a new concept.
 21. The system of claim 15,wherein the multimedia content item is any of: an image, a graphic, avideo stream, a video clip, a video frame, an audio clip, and aphotograph.
 22. A system for identifying multimedia content elements,comprising: a processing unit; a memory connected to the processingunit, the memory containing instructions that when executed by theprocessing unit, configure the system to: generate a signature for anunknown multimedia content element (MMCE), wherein the unknown MMCE isat least a portion of a multimedia content item; determine whether theunknown MMCE is identifiable by the signature; upon determining that theunknown MMCE is identified by the signature, identify the MMCE based onthe signature; and upon determining that the MMCE is not identified bythe signature: compare a signature of the unknown MMCE to a signaturecluster of at least one concept to determine whether each concept isproximate to the unknown MMCE; for each proximate concept, determine aprobability that the at least one proximate concept identifies the MMCE;and identify the MMCE based on the determined probabilities of the atleast one proximate concept.
 23. The system of claim 22, whereinidentifying the unknown MMCE further comprises: determining whether ahighest probability among the determined probabilities is below apredefined identification threshold; and upon determining that thehighest probability is below the predefined identification threshold,identifying the MMCE as unknown.
 24. The system of claim 22, whereinidentifying the unknown MMCE further comprises: determining a pluralityof proximate concepts having the highest probabilities among the atleast one proximate concept, wherein the MMCE is identified aspotentially being related to each of the plurality of proximateconcepts.
 25. The system of claim 22, wherein each probability isdetermined to be equal to a proportion between the number of signaturesof the proximate concept that match the signature of the MMCE and thetotal number of signatures of the proximate concept.
 26. The system ofclaim 22, wherein the multimedia content item is any of: image, agraphic, a video stream, a video clip, a video frame, and a photograph.